Although predictions and forecastings based on network embeddings from the above methods gives promising results, the real world networks are complex and available in multiple forms. Some of such complex networks include temporal networks [1]}, sign network [2]} and multi-layer network [3]}. Learning representations from such networks can improve prediction and forecasting power of a learning algorithm. We focus on feature representations on a special kind of multilayer network called multiplex network. The key challenge involved in learning features from a multiplex network is to consider learning from multiple layers or networks to give a single representation. The very naive approach of extracting features from the multilayer network is to merge all networks into a single network [3]}, [5]}. Combining multiple networks into a single network may disturb the topological order or properties of nodes with respect to each network in the multiplex network [6]}. To overcome this limitation, a hierarchy based model has been proposed to learn features from multilayer networks that uses Node2Vec [7]} to get embeddings of multiple networks and a hierarchy to combine such embeddings [8]}. The drawback of this approach is that it relies on a hierarchy of layers of networks to combine the results. However, in the real world multilayer networks such as social networks, such hierarchy does not exist and manually designing such hierarchies may not represent the network to the highest precision. Recently a Scalable Multiplex Network Embedding(MNE) [9]} has been introduced, which has performance overload since it extracts additional embeddings of nodes from multiple layers in the network. In this paper, we propose a simple random walk procedure, inspired from [10]}, to organize nodes across multiple networks and use such sequence of nodes to learn their low-dimensional features.
